Authored By
Cesar Estrada
Cesar oversees Arcesium's investment operations, accounting, and data management solutions for private markets fund managers and institutional investors.
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Private credit really took off after the global financial crisis. It also hardwired the operating model that came with it: corporate borrowers, periodic borrower reporting, quarterly closes, and systems built around one contained strategy. But now private credit firms are expanding further into new debt categories, including asset-based finance (ABF).
ABF raises novel challenges for firms accustomed to managing direct loans, creating different lifecycles, data, and stresses on how to manage investments, because ABF deals lend against various contractual cash flows, rather than direct repayments from a company's future cash flows.
Consumer ABF shows how these challenges affect data and technology, given their volumes and rate of change. With fintech platforms like SoFi, LendingTree, or Klarna, and 100+ more across consumer verticals, the fintech that originates the loan needs to service and finance each transaction. They can package those loans into a public or private securitization, or a private credit firm, a hedge fund, or an insurance company can finance them.
Deal structures, terms, periods, and amounts can vary. For example, an investor might make a billion-dollar commitment to a private credit firm that invests through multiple originators. An insurance company might underwrite and finance those loans directly. But within that consumer ecosystem, the operating rhythm looks different.
The massive daily flow of small consumer loans on fintech platforms implies massive amounts of data as consumers make new purchases, pay interest and principal, default, and buy more. The data comes in all forms, shapes, and frequencies, and it needs to be ingested, normalized, and validated for risk, portfolio management, accounting, and other analytical and reporting purposes.
This lifecycle affects the portfolio's exposure and returns every day, unlike a corporate book with monthly reporting. Given the rate of change in the consumer world, it also needs to be processed in real time so that investors can know what’s happening and what they can expect in terms of performance and risk management. They want faster, more granular monitoring that provides accurate bottom-up results, not just totals from the servicer.
Data is at the core of this segment. Given the volumes and rate of change, it needs to happen within a technology platform with minimal human intervention, not the homegrown spreadsheets that sometimes run large corporate loans.
Despite these differences, a lot of how ABF is done is dictated by how direct lending was done. Firms try to leverage system and process choices they made for a small book of loans. But as their ABF businesses become bigger, they need to rethink. What works for direct lending is not going to work for ABF.
For example, most private lenders started with better spreadsheets. Some loans still live in spreadsheets. Others are using legacy technologies built for their direct lending book, including Python scripts, legacy data warehouses, a hodgepodge of technologies, and a lot of human labor.
Additionally, many private credit firms used to operate on a quarterly cadence, close their books each quarter, and produce NAV and capital account statements. With ABF, the cadence and need for scrutiny raise the stakes. New pressures are also coming into play as firms pursue new investors along with new debt deals. Individual investors investing through wealth channels do so using regulated products instead of bespoke private limited partner arrangements.
The resulting vehicles demand more scrutiny and typically have a much higher reporting cadence. Such funds need to cut a NAV every day and offer some liquidity monthly or quarterly. Any errors matter because they’re trading off that NAV. As factors like reporting frequency, liquidity choices, volumes, and rate of change in investment strategies increase, this calls for robust modern technology.
They also call for operational change. Many of these firms have operated independently by business line, with direct lending, tradable credit, and ABF all separate. As they seek to offer clients a more consistent experience and create multi-asset products, they are bumping into a roadblock: reorganizing and centralizing fund accounting, investment operations, investor operations, performance analytics, and client reporting. They need enabling technology to do that as well.
Having a single source of truth across direct lending, ABF, and tradable credit means a single platform that can accommodate all of those nuances. In an ideal world, they’re using a modern technology platform with connectivity to these different platforms, ingesting the data systematically through APIs or some form of integration, then normalizing it. Reaching that point from their platforms today can be a struggle.
That source of truth serves different functional areas:
Even if ABF is forcing firms to take on complex platform challenges, the value of the effort makes them more competitive and more able to function and scale for the future. Having a good platform in place is essential to monitor and assess how risk is changing and pivot accordingly, and to deliver on the increased scrutiny from investors who expect greater transparency into portfolios more frequently. It accentuates the need for better data and technology.
Once they do so, better, more accurate, and more frequent data can help surface issues earlier, before drastic events like a large source of financing deciding you’re no longer creditworthy and closing the valve, triggering other sources to follow suit, and creating a liquidity challenge. The data pipes behind the scenes, data quality, and a governance framework can address those risks, and also lay the foundation for future usage of AI.
Because ABF offers a growth runway at least as large as that of direct lending, technology adoption has to increase faster.
